Karthik236990/Algerian-Forest-Fire-Prediction

# Algerian Forest Fire Prediction 🌲🔥 ## 📌 Project Overview The **Algerian Forest Fire Prediction** project is an end-to-end Machine Learning solution designed to predict the likelihood of forest fires based on various weather and environmental metrics. This repository demonstrates a complete machine learning lifecycle: from raw data ingestion and Exploratory Data Analysis (EDA) to feature engineering, model training, and finally serving the model via a production-ready web application using Flask, Docker, and Jenkins for Continuous Integration and Continuous Deployment (CI/CD). ## 📑 Table of Contents - Project Architecture - Repository Structure - Technologies Used - Getting Started - Prerequisites - Installation & Deployment - CI/CD Pipeline - Author ## 🏗️ Project Architecture 1. **Data Exploration & Engineering:** Comprehensive EDA and feature scaling performed in Jupyter Notebooks to prepare the Algerian Forest Fires dataset. 2. **Model Training:** Machine learning models evaluated and fine-tuned for high accuracy in predicting fire occurrences. 3. **Web Application:** A Flask API serves the trained model, providing a user-friendly interface for real-time predictions. 4. **Containerization:** The application is completely containerized using Docker for consistent cross-environment deployment. 5. **Automation:** Jenkins pipelines automate the build and deployment processes. ## 📂 Repository Structure ```text Algerian-Forest-Fire-Prediction/ ├── end_to_end_project_implimentation/ # Jupyter notebooks for EDA, preprocessing, and model training ├── flask/ # Flask application, routing, and HTML/CSS templates ├── Dockerfile # Instructions for building the Docker image ├── Jenkinsfile # CI/CD pipeline configuration ├── .gitignore # Ignored files and directories └── README.md # Project documentat …